In recent years, secure image archival and retrieval model in cloud computing (CC) received significant attention to ensure data confidentiality and secured data transmission among cloud server and clients cloud storage and users. Traditional secure image retrieval (IR) techniques are not applicable to adapt with a large-scale IR in cloud environment. In order to overcome these issues, this article

Nowadays, heterogeneous devices are widely utilizing Hypertext Transfer Protocol (HTTP) to transfer the data. Furthermore, HTTP adaptive video streaming (HAS) technology transmits the video data over wired and wireless networks. In adaptive technology services, a client’s application receives a streaming video through the adaptation of its quality to the network condition. However, such a technology

Person re-identification is a challenging research issue in computer vision and has a broad application prospect in intelligent security. In recent years, with the emergence of large-scale person datasets and the rapid development of deep learning, many outstanding results have been achieved in person re-identification researches, which mainly involves two critical technologies: feature extraction

The increasing performances of personal computers, as well as software of image processing, enable the easy manipulation of digital media content. Unfortunately, this easy manipulation makes the detection of changes in the multimedia content a very difficult task. Digital watermarking is among the most appropriate techniques to verify the integrity of multimedia content. In this paper, we propose a

Nowadays medical imaging plays a vital role in diagnosing the various types of diseases among patients across the healthcare system. Robust and accurate analysis of medical data is crucial to achieving a successful diagnosis from physicians. Traditional diagnostic methods are highly time-consuming and prone to handmade errors. Cost is reduced and performance is improved by adopting computer-aided diagnosis

The learning of agents in a competitive space such as a game is a challenging task. The aim of the proposed research is to improve the reinforcement learning techniques in a competitive multi-agent for the Polo game. First, the video dataset is prepared. Then, the rules of the Polo game are extracted as a class diagram. An architecture is designed for multi-agent team in the Polo game. Therefore, an

ResNet50 and VGG-16 models are introduced in this paper with different strategies, with and without preprocessing and with and without Support Vector Machine (SVM). Moreover, both transfer learning and data augmentation are used to solve the problem of lack of tagged data. The fully connected (FC) layer is replaced by the SVM classifier leading to better accuracy. In addition, in our work, we utilize

The quality of side information has an important impact on the performance of a distributed video coding system. At present, the generation of side information is mainly based on the translational motion model using the inter-frame correlation for motion estimation. Considering that the generated side information is prone to block effect and ghosting, as well as the situation that the nonlinear motion

We propose a new type of smart server based smart indoor crop grower using IoT(Internet of Things). The proposed smart indoor crop grower provides visual information for intuitively knowing the current cultivation status, as well as IoT-based sensor control, sensor data processing, communication control controller, smart server based sensor data storage, and building the optimal cultivation environment

Recently, developing more accurate and more efficient deep learning algorithms for medical images segmentation attracts more and more attentions of researchers. Most of methods increase the depth of the network to replace with acquiring multi-information. The costs of training images annotation are too expensive to label by hand. In this paper, we propose a multi-scale and better performance deep architecture

Bone age is an important measure for assessing the skeletal and biological maturity of children. Delayed or increased bone age is a serious concern for pediatricians, and needs to be accurately assessed in a bid to determine whether bone maturity is occurring at a rate consistent with chronological age. In this paper, we introduce a unified deep learning framework for bone age assessment using instance

Fruit detection plays a vital role in robotic harvesting platforms. However, natural scene attributes such as illumination variation, branch and leaf occlusion, clusters of tomatoes, shading, etc. and double scene including image augmentation and natural scene have made fruit detection a difficult task. An improved YOLOv3 model termed as Tomato detection models, which includes YOLODenseNet and YOLOMixNet

Automated benign and malignant breast masses classification is a crucial yet challenging topic. Recently, many studies based on convolutional neural network (CNN) are presented to address this task, but most of these CNN-based methods neglect the effective global contextual information. Moreover, their methods do not further analyze the reliability and interpretability of CNN models, which does not

Various biometric traits are available but recently finger knuckle image has attracted great attention to biometric research community due to its potentiality and ease of use. The performance of any biometric system heavily depends on the accuracy of feature detection and robustness of feature description. A number of feature descriptors are available but selection is being determined by the type of

Background subtraction from compressive measurements (BSCM) is a fundamental and critical task in video surveillance. Existing methods have limitations for incorporating the structural information and exhibit degraded performance in dynamic background, shadow and complex natural scenes. To address this issue, we propose a new Tucker decomposition-based sparse tensor optimization problem, which makes

Discrete orthogonal moments such as Meixner moments are powerful tools for characterizing image shape features for applications in pattern recognition and image classification. However, in the pattern recognition theory, classification of 2D/3D shapes regardless of their position, size, and orientation represents an important problem. In this paper, a new fast and accurate method is presented to obtain

Implicit Social Network is a connected social structure among a group of persons, where two of them are linked if they have some common interest. One real-life example of such networks is the implicit social network among the customers of an online commercial house, where there exists an edge between two customers if they like similar items. Such networks are often useful for different commercial applications

This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based

Recently biometric systems have shown improved capabilities because of the remarkable success of deep learning in solving various computer vision tasks. In ear recognition, the use of deep learning techniques is seldom due to training data scarcity. The existing work has shown poor performance as the majority of techniques are based on either handcraft features or pre-trained models. Besides this,

The synthetic aperture radar (SAR) images are playing an essential role in remote sensing. Various types of internal, external, and environmental noise are affecting the SAR images. Coherent speckle noise is the primary source of noise in SAR images. Such noise can be removed by using a single filter or combination of filters and transform signals. SAR image denoising has been attracting the attention

In this paper a novel decision based iterative filter for the detection and elimination of salt and pepper noise is proposed. Effective decisions based upon the noise density of the image are used to filter out noise while maintaining finer details in an image. A fixed size window is used at each step to maintain maximum correlation throughout the filtering process. Additional pre-edge and post-smoothing

Intensity inhomogeneity and noises often occur in real medical images, which present a large degree of challenge to image segmentation. At the same time, most of the existing image segmentation algorithms are sensitive to initial conditions and model parameters. This paper presents an accurate and robust active contour model to solve the above problems. Inspired by the idea of the region-scalable fitting

Creating different textile patterns to generate printable fabric images is a difficult image processing task. To accomplish this task, we propose a novel framework for dyeing creation, which allows non-professionals to design individual fabric images. The two main components of this framework are textile pattern discovery and fabric image generation. Since the objects in the fabric image are multi-category

In the telemedicine industry, a standout among the most significant issue is the exchange of Electronic Patient Information (EPI) between patient and a doctor that are remotely connected. A minute change to EPI may result in a wrong diagnosis for the patient. To ensure secure and safe communication for telemedicine applications, a dual-image separable block-based reversible data hiding algorithm in

Recently coronavirus 2019 (COVID-2019), discovered in Wuhan city of China in December 2019 announced as world pandemic by the World Health Organization (WHO). It has catastrophic impacts on daily lives, public health, and the global economy. The detection of coronavirus (COVID− 19) is now a critical task for medical specialists. Laboratory methods for detecting the virus such as Polymerase Chain Reaction

Image colorization is the process of assigning different RGB values to each pixel of a given grayscale image to obtain the corresponding colorized image. In this work, we propose a new automatic image colorization method based on the modified cycle-consistent generative adversarial network (CycleGAN). This method can generate a natural color image with only one given gray image without reference image

Sensitive multimedia information of all forms is encrypted, with key, before storage and transmission, to protect from illegal use and data manipulation. Since digital images are larger, it’s crucial to encrypt the content, specifically in medical images effectively. In medical image diagnosis, even a small manipulation of data may lead to misinterpretation. This paper addresses this concern by devising

In the field of unsupervised learning, Self-Organizing Map (SOM) has attracted the attention of many researchers. SOM is a popular algorithm in the area of data clustering; in this paper, new algorithms are developed to find the initial weights, to assign those initial weights to the SOM grid and a new way to determine the number of clusters in the SOM algorithm. Also, a new performance measure MSRI

Memory related issues in brain are mainly caused by Alzheimer disease (AD) which is the most common form of dementia. This disease must be diagnosed in its prodromal stage known as Mild Cognitive Impairment (MCI) also it needs an accurate detection and classification technique. In this paper, a computer-aided diagnosis (CAD) system is implemented on Magnetic resonance imaging (MRI) data from ADNI database

Accurate diagnosis of thyroid nodules using ultrasonography heavily relies on the superb skills and rich experience of senior radiologists, considering the low contrast, high noise of the ultrasound image, and the diverse appearance of the nodules. Computer-aided diagnosis systems could diagnose thyroid nodules based on ultrasound characteristics to assist radiologists. However, the existing learning-based

Deep learning models have already benchmarked its demonstration in the applications of Medical Sciences. Present day medical industries suffer due to deadly disease such as malaria etc. As per the report from World Health Organization (WHO), it is noted that the amount of caution and care taken per patient by a human doctor to cure malaria is decreasing. To address this issue, this paper proposes an

This paper presents a new segmentation algorithm and deep learning concept for innovative copy-move image forgery identification. In this work, the segmentation algorithm uses a new Adaptive Harris Hawk Optimization (AHHO) algorithm. The host image is segmented into irregular and non-overlapping blocks, named as Image Blocks (IB). Here the segmented image is compressed with a Discrete Cosine Transform

The communication between a person from the impaired community with a person who does not understand sign language could be a tedious task. Sign language is the art of conveying messages using hand gestures. Recognition of dynamic hand gestures in American Sign Language (ASL) became a very important challenge that is still unresolved. In order to resolve the challenges of dynamic ASL recognition, a

The paper mainly analyzes four typical reversible data hiding (RDH) algorithms that use PEE technology in combination: Conventional Prediction-error-expansion(C-PEE), Adaptive Prediction-error-expansion(A-PEE), Pairwise Prediction-error-expansion (P-PEE), Improve Pairwise Prediction-error-expansion(IP-PEE). And the experimental comparison results are also given. In addition, in the research process

Recently, Bayesian Optimization (BO) provides an efficient technique for selecting the hyperparameters of machine learning models. The BO strategy maintains a surrogate model and an acquisition function to efficiently optimize the computation-intensive functions with a few iterations. In this paper, we demonstrate the utility of the BO to fine-tune the hyperparameters of a Random Forest (RF) model

Recently, biometric-based security plays a vital role in the success of the Cognitive Internet of Things (C-IoT) based security framework. The iris trait solves a lot of security issues, especially in smart IoT-based applications. It increases the resistance of these systems against severe authentication attacks. In this paper, an efficient iris recognition model based on chaotic encryption and deep

Nowadays, data hiding techniques are one of the most growing researching areas. This paper introduces a quadruple histogram shifting-based reversible data embedding approach for digital images. Pixel differences distribution is utilized for maximizing the hiding payload capacity while preserving minimum degradations for stego-images. The improved histogram-based reversible data embedding algorithm

With the aim to ensure privacy and security of secret message, an enhanced reversible data hiding algorithm in encrypted as well as compressed domain is proposed here. After compression of grey-scale image using absolute moment block truncation coding (AMBTC) method, pixels of AMBTC compressed image is segmented into two types - seed pixels (2k…255) and remaining pixels are called non-seed pixels.

Various technologies have been utilized today for recognizing client or user in the indoor areas. These technologies incorporate RSSI, Bluetooth Low Energy Beacons, Ultrasound waves, Vision-based advances, for example, fixed camera recordings QR codes, remote gadgets, etc. RSSI fingerprinting technique requires more effort and it is also expensive to be used for indoor localization frameworks working

Significant growth has been made with multi-modal data as its entrance in the field of deep learning; whereas, Convolutional Neural Network (CNN) provides sufficient training data to develop a representative encrusted image. Yet, the multi-modality approach in CNN affect the performance by slowly converge the variance along with high-dimensionality, heterogeneity and non-aconvex optimization problems

Objective evaluation of images is one of the most essential and practical aspects of image inpainting. The existing objective evaluation methods of image inpainting are functional only on an individual basis and do not provide an accurate and useful objective evaluation of inpainted images. Currently, there is no objective measure for evaluating inpainted images. In this study, an objective evaluation

Automatic differentiation of benign and malignant breast lesions on multiple DCE-MRI series is a challenging task. The performance of the Convolutional Neural Networks (CNNs) based methods is severely affected when the number of DCE-MRI series is inadequate or inconsistent. This paper is motivated by the need of capturing spatial-temporal features from consistent DCE-MRI series for most CNN-based classification

Skin cancer is a type of dangerous disease, and early detection is necessary to increases the survival rate. In recent years, deep learning models applied to computerized skin cancer discovery has become a standard. These models can improve their performance by being able to access more data and its main task is to the classification of images. This task is exceptionally valuable in the field of medicine

Face recognition is one of the most important and widely applicable research problems in the subject area of machine learning and computer vision. Extraction of features, local or holistic, is the fundamental step and subspace method has been a natural choice for facial feature extraction. Among these, methods like PCA, ICA, LDA aim to reduce the dimension of the data while retaining the statistical

The spare parts demand forecasting is very much essential for the organizations to minimize the cost and prevent the stock outs. The demand of spare parts/ car sales distribution is an important factor in inventory control. The valuation of the demand is challenging as the automobile spare parts/car sales demand are often recurrent. The renowned empirical method adopts historical demand data to create

3D point cloud based object recognition becomes increasingly important in the last few years, as the widely use of point cloud over the low-cost 3D sensors have developed rapidly. However, the obtained 3D point cloud is inevitably contaminated with noise due to physical and environmental factors, which has a negative impact on recognition task. To address this problem, a complete object recognition

Cluster based routing approaches have been researched extensively for saving energy of wireless sensor networks (WSNs). In a cluster based routing mechanism, cluster heads (CHs) cooperate mutually to forward their data to the base station (BS) through multi-hop fashion. Due to this process, CHs near to the BS loaded with huge relay traffic and tend to die quickly, which causes partition of the network

In this article, we have designed a new information confidentiality mechanism based on the combination of Blowfish encryption algorithm along with Henon and Chen chaotic dynamical systems. We have authenticated our proposed encryption algorithm over satellite and other standard digital images of image processing. Our proposed encryption algorithm is equally deployed as an encrtpter and decrypter while

Medical images exchanged over vulnerable networks are sensitive to modifications or tampering which could lead to misdiagnosis, risking the lives of the patients. Therefore, to provide safe and secure transmission of medical images, key security requirements such as authenticity, integrity, and confidentiality must be addressed. In this paper, a novel and efficient algorithm is proposed which provides

Increasing the popularity of cloud computing raises the importance of efforts to improve the services of this paradigm. Searching over encrypted data is a requirement for cloud storage to provide, in addition to privacy-preserving, convenient and low-cost access to some of the outsourced data. Security and functionality along with efficiency are important characteristics of searchable encryption schemes

One of the known problems in security systems is to identify persons based on certain signatures. Biometrics have been adopted in security systems to identify persons based on some physiological or behavioral characteristics that they own. Photoplethysmography (PPG) is a physiological signal that is used to describe the volumetric change of blood flow in peripherals with heartbeats. The PPG signals

Developing automated systems to detect and track on-road vehicles is a demanding research area in Intelligent Transportation System (ITS). This article proposes a method for on-road vehicle detection and tracking in varying weather conditions using several region proposal networks (RPNs) of Faster R-CNN. The use of several RPNs in Faster R-CNN is still unexplored in this area of research. The conventional

The major function of heart is to pump blood to tissues and organs necessary for the body metabolism. It is therefore one of the organs that affects human life. However, adverse situations, such as paralysis and death are the major problems that can lead to a heart failure. Healthy heart is very important to live comfortably. To prevent adverse events, it is important to monitor and detect heart diseases

In this work, we propose a new set of \(H_{\infty }\) deconvolution filtering of 2-D color image using feature extraction of local descriptor and Fornasini-Machesini II (FM-II) model. The principal goal is to design 2-D deconvolution filter to reconstruct the noisy color image with the minimal information extracted from local Krawtchouk moment, Moreover, the filtering error system is asymptotically

Fusion of infrared and visible image is a technology which combines information from two different sensors for the same scene. It also gives extremely effective information complementation, which is widely used for the monitoring systems and military fields. Due to limited field depth in an imaging device, visible images can’t identify some targets that may not be apparent due to poor lighting conditions

In this work, we present a variational level set model with closed–form solution via combining with the fuzzy clustering method for robust and efficient image segmentation. For the designed energy functional, the two region parameters are first quickly pre–computed by means of the fuzzy c–means method and then embedded into a variational binary level set framework. Unlike the traditional variational

3D-high efficiency video coding (3D-HEVC) is the latest new extension standard of high efficiency video coding (HEVC) for multi-view video plus depth (MVD). Several novel coding tools are employed in the 3D-HEVC for better representation of the dependent texture and depth video. However, employing these tools causes a significant increase in coding complexity. In this paper, we introduce a fast coding

Human vision easily identifies the structural degradation in a video and thus perceptual quality improvement is necessary. To adjust the video quality to match with the human vision we proposed a structural similarity (SSIM) based rate control algorithm for the 3D video. We incorporated a structural similarity index (SSIM) as the quality metric in rate control algorithm. The rate-distortion model and

Compiling and managing huge e-commerce catalogs is a hard and time-consuming task for a retailer. In particular, deriving standardized and structured descriptions from unstructured data modalities, such as texts and images, is crucial to the performance of search engines and the general organization of virtual store databases. In this paper, we propose methodologies and strategies based on Deep Learning